Forecasting the Short-Term Traffic Flow in the Intelligent Transportation System Based on an Inertia Nonhomogenous Discrete Gray Model

Duan, Huiming; Xiao, Xinping; Pei, Ling-Ling

doi:10.1155/2017/3515272

Cited by 10 publications

(5 citation statements)

References 29 publications

(33 reference statements)

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“…To build a more general nonlinear grey forecasting model, Ma et al [27] proposed a novel multivariate nonlinear grey Bernoulli model (NGBMC(1,n)), which can be considered as a combination of the NGBM(1,1) and the GMC(1,n) with different power parameters. More recent research results can be seen in [28][29][30][31].…”

Section: Introductionmentioning

confidence: 89%

A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP

Zhang

Zheng

2019

Complexity

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The nonlinear grey Bernoulli model, abbreviated as NGBM(1,1), has been successfully applied to control, prediction, and decision-making fields, especially in the prediction of nonlinear small sample time series. However, there are still some problems in improving the prediction accuracy of NGBM(1,1). In this paper, we propose a novel optimized nonlinear grey Bernoulli model for forecasting Chinaʼs GDP. In the new model, the structure and parameters of NGBM(1,1) are optimized simultaneously. Especially, the latest item of first-order accumulative generating operator (1-AGO) sequence is taken as the initial condition, then background value is reconstructed by optimizing weights of neighbor values in 1-AGO sequence, which is based on minimizing the sum of absolute percentage errors, and finally, we establish the new model based on the rolling mechanism. Prediction accuracy of the proposed model is investigated through some simulations and a real example application, and the proposed model is applied to forecast the annual GDP in China from 2019 to 2023.

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Section: Introductionmentioning

confidence: 89%

A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP

Zhang

Zheng

2019

Complexity

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“…• ARIMA: We iterate over all sensors and all test examples. In each iteration, we train an ARIMA model 13 , using the previous T ′ = 100 values as the training input. • Feed Forward Neural Network (FFNN): We implement an FFNN, where the input consists of the previous ′ readings across all sensors s ∈ S. The model produces predictions for the next T forecasting horizons.…”

Section: Forecasting Modelsmentioning

confidence: 99%

“…Identifying the future state of a system via forecasting has been applied in a wide range of disciplines such as economics [27], energy and environmental studies [3,16,22,36], epidemiology [21,46] and transport [13,32,41,49,55], among others. Forecasting is typically undertaken with the use of statistical and machine learning models, which may be embedded in an end-to-end forecasting system.…”

Section: Related Workmentioning

confidence: 99%

Foresight Plus: Scalable Serverless Real-Time Spatio-Temporal Traffic Forecasting

Oakley,

Conlan,

Demirci

et al. 2023

Preprint

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Building a real-time, cost-effective, spatio-temporal forecasting system is a challenging problem with many practical applications such as traffic and road network management. Most forecasting research focuses on average prediction quality, with less attention paid to building practical pipelines and achieving timely and accurate forecasts when the network is under heavy load. Additionally, transport authorities need to leverage dynamic data sources (e.g., scheduled roadworks) and vehicle-level flow data, while also supporting ad-hoc inference workloads at low cost. The cloud-based system Foresight, developed in collaboration with Transport for the West Midlands (TfWM), is able to ingest, aggregate and process streamed traffic data, as well as dynamic urban events/flow data to produce regularly scheduled forecasts with high accuracy. In this work, we extend our system with several novel enhancements. First, we present an efficient method for extending the forecasting scale, enabling transport managers to predict traffic patterns further into the future than existing methods. In addition, we augment the existing inference architecture with a new, fully serverless design. This offers a more cost-effective inference solution, which seamlessly handles sporadic inference workloads over multiple forecasting models. We observe that Graph Neural Network (GNN) forecasting models are robust to extensions of the forecasting scale, achieving consistent (and sometimes even improved) performance up to 24 hours ahead. This is in contrast to the 1 hour forecasting horizons popularly considered in the literature. Further, our serverless inference solution is shown to be significantly more cost-effective than provisioned alternatives in appropriate use-cases. We identify the optimal memory configuration of serverless resources to achieve an attractive cost-to-performance ratio.

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“…en, this idea was further applied to the short-term traffic flow prediction problem [30,31]. According to the grey system theory, the effect of new information is greater than that of old information.…”

Section: Introductionmentioning

confidence: 99%

A Novel Weighted Fractional GM(1,1) Model and Its Applications

et al. 2020

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Based on the ideas of the new information priority principle and the fractional-accumulation generating operator, in this paper we propose a novel weighted fractional GM(1,1) (WFGM(1,1)) prediction model. In the new model, the original sequence is first transformed by using the weighted fractional-accumulation generating operator, which involves two parameters. With special choices of these parameters, the proposed WFGM(1,1) model reduces to the classical GM(1,1) model and the fractional GM(1,1) (FGM(1,1)) model, as well as the new information priority GM(1,1) (NIPGM(1,1)) model studied recently. Stability property of the WFGM(1,1) model is studied in detail. In practice, the quantum particle swarm optimization algorithm is adopted to choose the quasi-optimal parameters for the new model so as to get the best fitting accuracy. Finally, four numerical examples from different practical applications are present. Numerical results show that the new proposed prediction model is very efficient and has both the best fitting accuracy and the best prediction accuracy compared with the GM(1,1) and the FGM(1,1) as well as the NIPGM(1,1) prediction models.

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Forecasting the Short-Term Traffic Flow in the Intelligent Transportation System Based on an Inertia Nonhomogenous Discrete Gray Model

Cited by 10 publications

References 29 publications

A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP

A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP

Foresight Plus: Scalable Serverless Real-Time Spatio-Temporal Traffic Forecasting

A Novel Weighted Fractional GM(1,1) Model and Its Applications

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